Instructions to use vietrix/viena-60m-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vietrix/viena-60m-pretrain with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vietrix/viena-60m-pretrain")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vietrix/viena-60m-pretrain")
model = AutoModelForCausalLM.from_pretrained("vietrix/viena-60m-pretrain")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vietrix/viena-60m-pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vietrix/viena-60m-pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vietrix/viena-60m-pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vietrix/viena-60m-pretrain

SGLang

How to use vietrix/viena-60m-pretrain with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vietrix/viena-60m-pretrain" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vietrix/viena-60m-pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vietrix/viena-60m-pretrain" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vietrix/viena-60m-pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vietrix/viena-60m-pretrain with Docker Model Runner:
```
docker model run hf.co/vietrix/viena-60m-pretrain
```

Viena 60M (Pretrain)

Model details

Developed by: Vietrix
Model type: decoder-only causal LM (Llama-style)
Parameters: ~60M
Layers: 16
Hidden size: 512
Attention heads: 8 (KV heads: 4)
Max sequence length: 1024
RoPE theta: 10000
Normalization/MLP: RMSNorm + SwiGLU
Precision: BF16 training

Tokenizer

SentencePiece BPE
Target vocab in config: 32k
Actual vocab in tokenizer.model: 2105 (trained on a small corpus)
Note: embeddings are sized for 32k; only the first 2105 tokens are used by the tokenizer.

Training data

Internal synthetic Vietnamese pretrain corpus.
Domains: Vietnam/general, math, code, identity.
Raw JSONL entries: ~2.4k; after cleanup/dedupe, HF dataset contains 472 unique texts.
PII: best-effort redaction during dataset build.

Training procedure

Objective: next-token prediction with packed sequences.
Sequence length: 1024.
Global batch size: 64 (batch 16 x grad_accum 4).
Optimizer: AdamW, lr 3e-4, weight decay 0.1, cosine decay with 10% warmup.
Steps: 2,500 (approx 163.8M tokens processed).
Checkpoints saved every 1,250 steps.

Intended use

Base model for continued training or fine-tuning on Vietnamese tasks.
Not instruction-tuned; outputs may be unaligned.

Limitations

Trained on a small synthetic corpus; coverage and factuality are limited.
Not suitable for safety-critical or high-stakes applications.
Tokenizer vocab is much smaller than model vocab; lexical coverage is limited.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "vietrix/viena-60m-pretrain"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_id)

If AutoTokenizer fails, load the SentencePiece model explicitly:

from transformers import LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained(model_id, use_fast=False)

Downloads last month: 1

Safetensors

Model size

66.7M params

Tensor type

F32

Model tree for vietrix/viena-60m-pretrain

Finetunes

1 model